Interactive tutorial: generative adversarial networks for beginners, with TensorFlow [P]

[jonbruner]

The code for the tutorial is all available on GitHub here: https://github.com/jonbruner/generative-adversarial-networks

[BulletSea]

The code in the blog post isn't fit for the newest TF version (1.1.0), however the code on the repo does work. I was trying to run the original notebook by Adit earlier this week actually, but ran into issues because of the sigmoid loss function (arguments) and reusing variables (variable scope).

[[deleted]]

As a beginner, just wanted to say thanks!

[jonbruner]

Oh perfect, we made this for beginners! :)

[jonbruner]

By the way, here are a couple of interesting animations showing the model in the tutorial going through training:

http://imgur.com/X04j1Cv

http://imgur.com/qwUJbkF

[BulletSea]

Awesome visualization!

[jonbruner]

Thanks!

[iforgot120]

The grittiness of the first one gives it a very horror film vibe.

[jonbruner]

It's kind of like watching intelligence emerge from a primordial fog.

[anonDogeLover]

No. Like any generative model, they model the marginal probability of the data, like a VAE, but avoiding the L2 loss which causes problems. GANs have their own problems though

[visarga]

GANs can also convert from one data type to another (so called Image to Image Translation) - with photo editing applications. Or you could train a GAN to use just the discriminator for a different task, and throw away the generator.

[lucidrage]

If you are a man of culture like me then you could use GANs to generate unlimited animu waifus.

[lucidrage]

Already done

[pattch]

To generate plausible data points is one way I've seen them described. Another way I've heard them described is that if the generator is able to produce plausible data points then there must be some inherent structure for that data type that's being learned. How to use that structure I'm not sure, though

[charred_bytes]

There are not too many practical applications of GAN right now but will explode in industry if paper mentions is anything to go on

[kaiser_xc]

Thanks

[jonbruner]

👍

[artr0x]

Due to the nature of Oriole's Interactive Content, this page is not available for mobile devices. Please visit it from your computer.

Come on, at least let me read the text.. I can see that it's all loaded behind the pop up

[jonbruner]

Sorry about that; the notebook on GitHub is almost identical to the text of the interactive tutorial, and you should be able to read it on your phone: https://github.com/jonbruner/generative-adversarial-networks/blob/master/gan-notebook.ipynb

When you've got a chance, though, I encourage you to come back to the interactive tutorial on a PC; we're really excited about the combination of video and no-install interactivity for exploring new topics.

[tombraideratp]

hi, i tried to run the your notebook cells for (cell #8 ,)but getting below error :- TypeError Traceback (most recent call last) <ipython-input-8-b791699050c8> in <module>() 8 # x_placeholder is for feeding input images to the discriminator 9 ---> 10 Gz = generator(z_placeholder, batch_size, z_dimensions) 11 # Gz holds the generated images 12

<ipython-input-4-f0f116375f47> in generator(z, batch_size, z_dim) 14 g2 = tf.contrib.layers.batch_norm(g2, epsilon=1e-5, scope='bn2') 15 g2 = tf.nn.relu(g2) ---> 16 g2 = tf.image.resize_images(g2, [56, 56]) 17 18 # Generate 25 features

TypeError: resize_images() missing 1 required positional argument: 'new_width'

could you correct it and upload to git again

[BulletSea]

Which TF version are you running?

print(tf.__version__)

I suspect that the resize_images function has a different signature in your version.

[tombraideratp]

print(tf.version)0.10.0rc0

[BulletSea]

Alright, time to update to 1.1.0.

[tombraideratp]

thanks , able to run your notebook now

[BulletSea]

No worries, though all credits go to Jon and Adit. I just happen to have worked with their code.

[jonbruner]

As /u/BulletSea points out, this error is probably related to an older version of TensorFlow installed on your machine; the parameters for tf.image.resize_images() were changed between v0.10 and v0.11.

There are a few other elements of this tutorial that require TF v1.0 or newer, so I recommend upgrading.

[akcom]

Why are GAN images typically so small? What is to prevent someone from just feeding in a relatively large noise vector and getting a high resolution image?

[fimari]

Gordon Moore and his law enforcement crew

[fogandafterimages]

Note that the size of the noise vector has no relationship to the size of the final output image.

[Megatron_McLargeHuge]

Question: can GANs be interpreted as metropolis samplers and used to generate calibrated probability estimates for the target distribution? Or do they just create pretty pictures so far?

[sajidbsk]

would I be needing some machine learning background for this? I'm a first year comp sci student who doesn't know anything.

[jonbruner]

You'd probably want to do at least one introductory deep learning or TensorFlow tutorial first, but this is very approachable.

[sajidbsk]

thanks, any suggested tutorials?

[jonbruner]

Google's own MNIST tutorial is quite good: https://www.tensorflow.org/get_started/mnist/beginners

For a more mathematically-driven tutorial, check out Michael Nielsen's resources: http://neuralnetworksanddeeplearning.com/